Modern DOE for Process Optimization (MDOE), $1695

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In this 3-day workshop participants learn to set up, analyze, and interpret DOE's ranging from fractional factorials to response surface designs. First discover the vital few effects along with unknown interactions, and then transition to RSM to optimize your process. Learn how to use the latest small-run DOE techniques to solve your real-world problems!

Attend the first 2 days at a reduced rate. Please inquire for this option.

Price includes a $95 fee for workshop materials which is subject to state and local taxes.

A 10% discounted Early Bird rate applies to registrations made 6 weeks prior to the workshop date.

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Modern DOE for Process Optimization (MDOE) (3 days)

Use State-of-the-Art DOE Tools to Make Breakthrough Improvements and Optimize the Final Results

This updated revision of our factorial and response surface methods workshops combines the topics and brings new insights into one fast-paced course! Find out how to make breakthrough improvements using powerful design of experiments (DOE) techniques. Start by learning about using factorial designs for finding which factors you need to focus on. Discover previously unknown interactions that often prove to be the key to success. Learn how to best use modern small-run designs to save time and money experimenting. Transition from factorials to response surface methods to optimize your products and processes. Tie all of this together by learning how to use powerful ANOVA analysis methods that give you confidence in your findings.

Apply Tried and True Techniques

This workshop covers the practical aspects of DOE. (Students may purchase the optional "DOE Simplified" book for reference.) You learn all about simple but powerful two-level factorial designs. During this DOE workshop, you will discover how to effectively:

Understand the motivation for factorial designs

Implement the DOE planning process

Interpret analysis of variance (ANOVA)

Discover hidden interactions

Capitalize on efficient small-run fractional designs for screening or characterization

Use power to properly size designs

Determine when to use transformations

Explore multilevel categoric factors

Set up modern split-plot designs - both factorial and RSM

Follow the strategy of experimentation from screening to response surface methods

Set up central composite (CCD), Box-Behnken and Optimal RSM designs

Select appropriate regression models

Optimize multiple responses numerically

Add multilinear constraints and categoric factors to optimal designs

Choose the Best Strategy

The "Modern DOE for Process Optimization" workshop helps you plan your DOE by selecting the appropriate designs. It guides you through your experiment and strategic analysis.

"Practical. Good mix of theory and application."—Chris Easter, Metallurgist

Simulations Provide Practice

Use Design-Expert® software to practice designing and analyzing experiments throughout the workshop. The software provides easy-to-use graphical tools to find key variables and view results. You will be given a path to all simulation and data files used in class, which are posted to a special Internet site where you can also link to a free fully-functional, but time-limited, copy of Design-Expert software for use after class.

"Gives you the 'hands-on' that puts it all together."—Matt Hanken, Senior Manufacturing Engineer

Course Outline

Day 1

Section 1—Introduction to Factorial Design

Background and motivation for factorial designs

Factorial design planning process

Basics of factorial design: Case study

Selecting effects—Half-normal plot and Pareto chart

ANOVA and residual diagnostics

Main effects, interaction, contour and 3D surface plots

Introduction to multiple-response optimization

Section 2—Enhancements for Design and Analysis of Factorials

Replicated 23full factorial: Case study

Explanation of power

24 full factorial: Exercise

Transformations: Case study

Dangers of deleting outliers

Details of using transformations

Section 3—Blocking and Fractionating Factorials

How to set up optimal blocking: Case study

Factors interacting vs three-factor interactions (3FIs)

How to set up fractional factorials

Understanding aliases

25-1 fractional factorial: Exercise

Day 2

Section 4—Modern Small-Run Designs

Minimum-run characterization (MR5) design: Case study

Minimum-run screening (MR4) design: Case study

Definitive screening (DSD) design

Guide to using small-run designs

Section 5—Multilevel Categoric Design (General Factorial)

Multilevel categoric design with replication: Case study

Fractionating via optimal (custom) design: Case study

Introduction to optimal (custom) design

Model graphs for multilevel categoric designs

Section 6—Split-Plot Designs

Restriction randomization

Split-plot design: Case study

Section 7—Factorial with Center Points and RSM Introduction

Factorial with center points: Case study

Introducing response surface methods (RSM)

Augmenting to central composite design (CCD): Case study

Day 3

Section 8—Response Surface Methods - Central Composite Designs

"Good" response surface designs

Response surface methods: Case study

Key differences in RSM analysis

Customized CCD's

Model reduction exercise

Section 9—Optimization and Confirmation

Optimization exercise

Numerical

Graphical

Exercise with confirmation runs

Section 10—Response Surface Designs

Box-Behnken design: Case study

Face-centered CCD (FCD)

Design Evaluation

Exercise

Modern composite design based on small-run core

Section 11—Optimal Design

Multiple linear constraints (MLCs): Case study

Optimal designs

Sizing for precision via fraction of design space

Categoric factors: Case study

RSM design summary

Section 12—Split-Plot Design for RSM

Review split-plot concepts

Central composite split plot

Optimal split plot

Summary

Optimize your process exercise

Mixture design overview

Next steps

Section 13—Optional RSM Tools (as time allows)

Sizing designs to detect a difference

User-defined candidate set

Design augmentation

MLCs revisited - more complex example

Prerequisites

Math skills, knowledge of basic statistics, and exposure to simple comparative experiments (e.g. two-sample t-test) are recommended. If you aren't ready for the Modern DOE for Process Optimization workshop, take the online PreDOE course first (a $95 value you get for free! It takes 2-3 hours to complete. You can work at your own pace). Access the PreDOE here.

Before attending class, please download a trial of DX10 (if you do not already have access to it) and work through the General Multilevel-Categoric One-Factor tutorial.

Additional Information

PDHs

24 (equals 2.4 CEUs)

Additional Information

Workshop location details will be provided with your confirmation letter, which you will receive once the minimum enrollment requirements are met.